- Date
- Thursday 4 Jul 2019, 16:00 - 17:00
- Type
- Seminar
- Spoken Language
- English
- Room
- EB-12
- Building
- E Building
- Location
- Campus Woudestein
We introduce a flexible Bayesian structural vector autoregressive model identified through heteroskedasticity, encompassing a range of volatility processes and allowing for additional identifying restrictions. Consequently, it enables comparisons across structural models with alternative sets of restrictions that just identify homoskedastic specifications. We develop a complete toolset for Bayesian inference, including a novel estimation algorithm, and an unbiased marginal data density estimator for locally identified models. Applying this apparatus to three U.S. monetary policy models, we document the empirical outperformance of models making use of two policy variables over those with a single one.
Co-author: Matthieu Droumaguet (Goldman Sachs, Hong Kong)
- More information
Coordinators: Andreas Alfons, alfons@ese.eur.nl and Wendun Wang, wang@ese.eur.nl
Contact: Anneke Kop, eb-secr@ese.eur.nl